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Formal Verification of the VAMP Floating Point Unit
 In CHARME 2001, volume 2144 of LNCS
, 2001
"... We report on the formal verification of the floating point unit used in the VAMP processor. The FPU is fully IEEE compliant, and supports denormals and exceptions in hardware. The supported operations are addition, subtraction, multiplication, division, comparison, and conversions. The hardware is v ..."
Abstract

Cited by 11 (6 self)
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We report on the formal verification of the floating point unit used in the VAMP processor. The FPU is fully IEEE compliant, and supports denormals and exceptions in hardware. The supported operations are addition, subtraction, multiplication, division, comparison, and conversions. The hardware is verified on the gate level against a formal description of the IEEE standard by means of the theorem prover PVS.
Numerical Replication of Computer Simulations: Some Pitfalls and How To Avoid Them
, 2000
"... A computer simulation, such as a genetic algorithm, that uses IEEE standard oatingpoint arithmetic may not produce exactly the same results in two dierent runs, even if it is rerun on the same computer with the same input and random number seeds. Researchers should not simply assume that the result ..."
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Cited by 5 (0 self)
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A computer simulation, such as a genetic algorithm, that uses IEEE standard oatingpoint arithmetic may not produce exactly the same results in two dierent runs, even if it is rerun on the same computer with the same input and random number seeds. Researchers should not simply assume that the results from one run replicate those from another but should verify this by actually comparing the data. However, researchers who are aware of this pitfall can reliably replicate simulations, in practice. This paper discusses the problem and suggests solutions.
Numerical Replication of Computer Simulations: Some Pitfalls and How To Avoid Them ∗
, 2000
"... A computer simulation, such as a genetic algorithm, that uses IEEE standard floatingpoint arithmetic may not produce exactly the same results in two different runs, even if it is rerun on the same computer with the same input and random number seeds. Researchers should not simply assume that the re ..."
Abstract
 Add to MetaCart
A computer simulation, such as a genetic algorithm, that uses IEEE standard floatingpoint arithmetic may not produce exactly the same results in two different runs, even if it is rerun on the same computer with the same input and random number seeds. Researchers should not simply assume that the results from one run replicate those from another but should verify this by actually comparing the data. However, researchers who are aware of this pitfall can reliably replicate simulations, in practice. This paper discusses the problem and suggests solutions. 1